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        "author": "Bryan Perozzi",
        "author_email": "bperozzi@cs.stonybrook.edu",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4"
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        "description": "===============================\nDeepWalk\n===============================\n\nDeepWalk uses short random walks to learn representations for vertices in graphs.\n\nUsage\n-----\n\n**Example Usage**\n    ``$deepwalk --input example_graphs/karate.adjlist --output karate.embeddings``\n\n**--input**:  *input_filename*\n\n    1. ``--format adjlist`` for an adjacency list, e.g::\n\n        1 2 3 4 5 6 7 8 9 11 12 13 14 18 20 22 32\n        2 1 3 4 8 14 18 20 22 31\n        3 1 2 4 8 9 10 14 28 29 33\n        ...\n\n    2. ``--format edgelist`` for an edge list, e.g::\n\n        1 2\n        1 3\n        1 4\n        ...\n\n    3. ``--format mat`` for a Matlab .mat file containing an adjacency matrix\n        (note, you must also specify the variable name of the adjacency matrix ``--matfile-variable-name``)\n\n**--output**: *output_filename*\n\n    The output representations in skipgram format - first line is header, all other lines are node-id and *d* dimensional representation::\n\n        34 64\n        1 0.016579 -0.033659 0.342167 -0.046998 ...\n        2 -0.007003 0.265891 -0.351422 0.043923 ...\n        ...\n\n**Full Command List**\n    The full list of command line options is available with ``$deepwalk --help``\n\nEvaluation\n----------\nHere, we will show how to evaluate DeepWalk on the *BlogCatalog* dataset used in the DeepWalk paper.\nFirst, we run the following command to produce its DeepWalk embeddings::\n\n    deepwalk --format mat --input example_graphs/blogcatalog.mat\n    --max-memory-data-size 0 --number-walks 80 --representation-size 128 --walk-length 40 --window-size 10\n    --workers 1 --output example_graphs/blogcatalog.embeddings\n\nThe parameters specified here are the same as in the paper.\nIf you are using a multi-core machine, try to set ``--workers`` to a larger number for faster training.\nOn a single machine with 24 Xeon E5-2620 @ 2.00GHz CPUs, this command takes about 20 minutes to finish (``--workers`` is set to 20).\nThen, we evaluate the learned embeddings on a multi-label node classification task with ``example_graphs/scoring.py``::\n\n    python example_graphs/scoring.py --emb example_graphs/blogcatalog.embeddings\n    --network example_graphs/blogcatalog.mat\n    --num-shuffle 10 --all\n\nThis command finishes in 8 minutes on the same machine. For faster evaluation, you can set ``--num-shuffle`` to a smaller number, but expect more fluctuation in performance. The micro F1 and macro F1 scores we get with different ratio of labeled nodes are as follows:\n\n+-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+\n| % Labeled Nodes | 10%   | 20%   | 30%   | 40%   | 50%   | 60%   | 70%   | 80%   | 90%   |\n+=================+=======+=======+=======+=======+=======+=======+=======+=======+=======+\n| *Micro-F1 (%)*  | 35.86 | 38.51 | 39.96 | 40.76 | 41.51 | 41.85 | 42.27 | 42.35 | 42.40 |\n+-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+\n| *Macro-F1 (%)*  | 21.08 | 23.98 | 25.71 | 26.73 | 27.68 | 28.28 | 28.88 | 28.70 | 28.21 |\n+-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+\n\n**Note that the current version of DeepWalk is based on a newer version of gensim, which may have a different implementation of the word2vec model. To completely reproduce the results in our paper, you will probably have to install an older version of gensim(version 0.10.2).**\n\nRequirements\n------------\n* numpy\n* scipy\n\n(may have to be independently installed) \n\n\n\nInstallation\n------------\n#. cd deepwalk\n#. pip install -r requirements.txt \n#. python setup.py install\n\n\nCiting\n------\nIf you find DeepWalk useful in your research, we ask that you cite the following paper::\n\n    @inproceedings{Perozzi:2014:DOL:2623330.2623732,\n     author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},\n     title = {DeepWalk: Online Learning of Social Representations},\n     booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\n     series = {KDD '14},\n     year = {2014},\n     isbn = {978-1-4503-2956-9},\n     location = {New York, New York, USA},\n     pages = {701--710},\n     numpages = {10},\n     url = {http://doi.acm.org/10.1145/2623330.2623732},\n     doi = {10.1145/2623330.2623732},\n     acmid = {2623732},\n     publisher = {ACM},\n     address = {New York, NY, USA},\n     keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},\n    } \n\nMisc\n----\n\nDeepWalk - Online learning of social representations.\n\n* Free software: GPLv3 license\n* Documentation: http://deepwalk.readthedocs.org.\n\n\n\n.. image:: https://badge.fury.io/py/deepwalk.png\n    :target: http://badge.fury.io/py/deepwalk\n\n.. image:: https://travis-ci.org/phanein/deepwalk.png?branch=master\n        :target: https://travis-ci.org/phanein/deepwalk\n\n.. image:: https://pypip.in/d/deepwalk/badge.png\n        :target: https://pypi.python.org/pypi/deepwalk\n\n\n\n\nHistory\n-------\n\n1.0.3 (2018-03-23)\n---------------------\n\n* Now compatible with the latest version of gensim and sklearn\n* Better support for Python 3\n\n1.0.2 (2014-09-19)\n---------------------\n\n* Fixed gensim at 0.10.2 for now\n\n1.0.1 (2014-09-19)\n---------------------\n\n* Added utilities to support generated embeddings for larger graphs\n* Support for additional input file formats\n\n1.0.0 (2014-08-24)\n---------------------\n\n* First release on PyPI.\n\n\n",
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